Prepare MODIS Data

This code imports, cleans, and merges MODIS product data exported using the AppEEARS web application.

Exported MODIS data for the period 2000-2016:

• MOD11A2: LST Day 1km & LST Night 1km (8 day)
• MOD13Q1: EVI 250m & NDVI 250m (16 day)
• MOD15A2H: fPAR 500m & LAI 500m (8 day)
• MOD16A2: LE 500m & PET 500m (8 day)
• MOD17A2H: GPP 500m (8 day)

Data was exported for 50 BAMS paper sites (.csv format, “ID”, “Category”, “LAT”, “LONG”)

9 MODIS products are downloaded in 5 .csv files:

[1] "./BAMS-Sites-MOD11A2-006-results.csv"  "./BAMS-Sites-MOD13Q1-006-results.csv"  "./BAMS-Sites-MOD15A2H-006-results.csv"
[4] "./BAMS-Sites-MOD16A2-006-results.csv"  "./BAMS-Sites-MOD17A2H-006-results.csv"
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NA

Simplify data by removing unnecssary fields:

$lst_day

$vi

$lai

$et

$gpp

$lst_night

NA

Look at what the data quality levels are, convert good to 1, keep only 1s

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Make a plot of daytime LST to make sure things look OK

ggplot(modis.filtered[[1]], aes(DATE, LST_D, col = as.factor(LAT))) +
  geom_point()

Create a dataframe for each product consisting of LAT, LONG, DATE and the product field (e.g. LST_D)

lst_d.filtered <- modis.filtered$lst_day %>% mutate(LST_D = LST_D) %>% select(LAT, LONG, DATE, LST_D)
lst_n.filtered <- modis.filtered$lst_night %>% mutate(LST_N = LST_N) %>% select(LAT, LONG, DATE, LST_N)
evi.filtered <- modis.filtered$vi %>% mutate(EVI = EVI) %>% select(LAT, LONG, DATE, EVI)
ndvi.filtered <- modis.filtered$vi  %>% mutate(NDVI = NDVI) %>% select(LAT, LONG, DATE, NDVI)
fpar.filtered <- modis.filtered$lai  %>% mutate(FPAR = FPAR) %>% select(LAT, LONG, DATE, FPAR)
lai.filtered <- modis.filtered$lai  %>% mutate(LAI = LAI) %>% select(LAT, LONG, DATE, LAI)
gpp.filtered <- modis.filtered$gpp %>% mutate(GPP = GPP) %>% select(LAT, LONG, DATE, GPP) 
le.filtered <- modis.filtered$et %>% mutate(LE = LE) %>%  select(LAT, LONG, DATE, LE) 
pet.filtered <- modis.filtered$et %>% mutate(PET = PET) %>% select(LAT, LONG, DATE, PET) 

Create a full dataframe with each site name/latlong repeated n = length(2000-01-01 to 2018-12-31) (MODIS data window). Then right_join each modis product to the site name/all dates tibble to produce a complete dataframe of modis data.

full.date <- as_tibble(rep(as_date(c(as_date("2000-01-01"):as_date("2018-12-31"))),50))
names(full.date) <- "DATE"
full.date <- full.date %>% add_column(ID = rep(bams.sites$ID, each = length(full.date$DATE)/50)) %>% 
                              add_column(LAT = rep(bams.sites$LAT, each = length(full.date$DATE)/50)) %>%
                                add_column(LONG = rep(bams.sites$LONG, each = length(full.date$DATE)/50))
modis.full <- right_join(full.date, lst_d.filtered, by =c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, lst_n.filtered, by=c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, evi.filtered, by=c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, ndvi.filtered, by=c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, fpar.filtered, by=c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, lai.filtered, by=c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, gpp.filtered, by=c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, le.filtered, by=c("DATE","LAT","LONG"))
modis.full <- right_join(modis.full, pet.filtered, by=c("DATE","LAT","LONG"))
modis.full <- left_join(modis.full, bams.sites, by=c("LAT", "LONG"))
modis.full <- modis.full %>% select(ID = ID.y, Category, -ID.x, LAT, LONG, DATE, LST_D, LST_N, EVI, NDVI, FPAR, LAI, GPP, LE, PET)
str(modis.full)

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   203430 obs. of  14 variables:
 $ ID      : Factor w/ 50 levels "BCBog","BCFEN",..: 13 13 13 13 13 13 13 13 13 13 ...
 $ Category: Factor w/ 5 levels "CRO_OTHER","CRO_RICE",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ LAT     : num  27.2 27.2 27.2 27.2 27.2 ...
 $ LONG    : num  -81.2 -81.2 -81.2 -81.2 -81.2 ...
 $ DATE    : Date, format: "2001-01-01" "2001-01-09" "2001-01-17" "2001-01-25" ...
 $ LST_D   : num  297 NA 298 NA NA ...
 $ LST_N   : num  278 NA 280 NA NA ...
 $ EVI     : num  0.208 NA 0.194 NA 0.184 ...
 $ NDVI    : num  0.331 NA 0.323 NA 0.333 ...
 $ FPAR    : num  0.36 0.33 0.33 0.36 0.3 ...
 $ LAI     : num  0.5 0.7 0.7 0.8 0.6 ...
 $ GPP     : num  0.0121 0.0153 0.0135 0.0158 0.0156 ...
 $ LE      : num  2430000 1820000 2020000 1800000 32765 ...
 $ PET     : num  75.6 26.6 27.1 30.4 32765 ...

modis.full

Finished! Save as .csv

# set wd
setwd("C:/Users/Gavin McNicol/Box Sync/MODIS Data/AppEEARS Data")
write.csv(modis.full, "bams_sites_MODIS_full.csv")